Real-Time Nonlinear Model Predictive Control for the Energy Management of Hybrid Electric Vehicles in a Hierarchical Framework

Proceedings of the ... American Control Conference/Proceedings of the American Control Conference, pp. 1961–1967

Abstract

In this paper a real-time capable hierarchical nonlinear model predictive control framework for the energy management of a hybrid electric vehicle is presented. As high-level energy management, nonlinear model predictive control is employed. Therefore, a nonlinear optimal control problem is formulated using a control-oriented internal model derived from high-fidelity models and experimental data. The multiple shooting algorithm and an Euler backward scheme are used to discretize the optimal control problem. The resulting nonlinear problem is solved in real-time using Sequential Quadratic Programming. A rule-based gear choice and engine on / off strategy is added. Actuator dynamics and drivability are addressed in a fast low-level linear time-variant model predictive controller. The result is analyzed and compared to the optimal solution obtained by dynamic programming using a simplified model of the vehicle.

Authors 4

1. Lukas Schmitt Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany

2. Martin Keller Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany

3. Thivaharan Albin

   ETH Zurich · Institute for Dynamic Systems and Control

   Affiliation as printed

   Institute for Dynamic Systems and Control, ETH Zurich, Zurich, Switzerland

4. Dirk Abel Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   Institute of Automatic Control, RWTH Aachen University, Aachen, Germany